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Aims Methods and results Conclusions The American Society of Clinical Oncology and College of American Pathologists HER2‐guidelines recommend repeat testing for most grade 1 mammary carcinomas that are HER2‐positive in the core biopsy. This study aimed to assess the value of repeat HER2‐testing and the histological features of HER2‐positive grade 1 carcinomas.A case‐series of HER2‐results of grade 1 carcinomas was conducted of patients with no pre‐operative systemic treatment over a 5‐year period. HER2‐positive carcinomas had histological review. Twelve HER2‐positive carcinomas were initially reported as grade 1. On review, two were reclassified as grade 2. The remaining 10 carcinomas represented 2% of the 508 grade 1 carcinomas. Eight HER2‐positive grade 1 carcinomas from other years were also studied. HER2‐positive carcinomas more often had marked nuclear pleomorphism (50 versus 6%) and were more often oestrogen receptor‐negative (17 versus 0.8%) and progesterone receptor‐negative (28 versus 8%) compared with HER2‐negative grade 1 carcinomas. Six carcinomas that were HER2 3+ in the core biopsy were also 3+ on repeat assessment. Five of seven carcinomas that were 2+ amplified in the core biopsy were also HER2‐positive in the excision.HER2‐positive grade 1 carcinomas are uncommon, and more often have marked nuclear pleomorphism and lack oestrogen receptor and progesterone receptor expression compared with HER2‐negative grade 1 carcinomas. A HER2‐poitive result in the core biopsy was confirmed in 11 of 13 tumours that had repeat testing. [ABSTRACT FROM AUTHOR]

Introduction: Breast cancer is a diverse and heterogeneous disease affecting women in both developing and developed countries. In 2022, Global Cancer Observatory (GLOBOCAN) reported 2.3 million cases of breast carcinoma, which accounted for 11.6% of all cancer cases worldwide. The incidence of breast cancer in India has been steadily rising and has surpassed cervical cancer as the most common cancer in women. Invasive ductal carcinoma is the most common histologic type found in all age groups. Grading is done according to Elston Ellis's modification of the Scarff Bloom Richardson Scoring system. Hormonal {Estrogen Receptor (ER), Progesterone Receptor (PR)} and Human Epidermal growth factor Receptor-2 (HER2/ neu) immunohistochemical expression are used for molecular typing of breast carcinoma. Young women account for up to 25% of all breast carcinoma cases in India. Aim: To assess the clinical symptoms, histological type, grade, Immunohistochemistry (IHC) profile (ER, PR, HER2neu), and their relationships in females below 40 years of age with breast carcinoma. Materials and Methods: An ambispective cross-sectional study with a total of 99 cases of breast carcinoma in young females were included in the study, which was conducted in the Department of Pathology at Sri Aurobindo Medical College and PG Institute in Indore, Madhya Pradesh, India, between April 2022 and March 2024. Biopsies, lumpectomy, and total or modified mastectomy specimens were fixed in formalin and paraffin embedded. Sections were stained with Haematoxylin and Eosin (H&E) and IHC markers ER, PR, and HER2neu using Estrogen Receptor Alpha Antibody (EP1), EP2, and EP3 antibodies, respectively, on a fully automated workstation. Carcinomas were classified according to the World Health Organisation (WHO) classification and graded based on Elston Ellis's modification of the Scarff Bloom Richardson System Scoring. The proportion of tumour cells and intensity were studied, and evaluation was done with appropriate controls. Results: The most common symptom was a breast lump (84.84%), while Invasive breast cancer of {No Special Type (NST)} was the most common histologic type (96.96%) and histological Grade III was predominant (53.53%) in the majority of patients. ER, PR, and HER2neu were positive in 57.57%, 51.51%, and 33.33% of cases, respectively. HER2neu expression was negative in the majority of cases (66.66%). The most common molecular subtype was Luminal A (35.35%), followed by Triple-negative Breast Cancer (TNBC) (29.29%), Luminal B (22.22%), and the least common was the HER2neu enriched subtype (13.13%). Conclusion: Invasive breast carcinoma (NST) is the most common histologic type, with higher histologic grade and a greater number of triple-negative cases seen in young females (<40 years). [ABSTRACT FROM AUTHOR]

Introduction: Breast cancer is a diverse and heterogeneous disease affecting women in both developing and developed countries. In 2022, Global Cancer Observatory (GLOBOCAN) reported 2.3 million cases of breast carcinoma, which accounted for 11.6% of all cancer cases worldwide. The incidence of breast cancer in India has been steadily rising and has surpassed cervical cancer as the most common cancer in women. Invasive ductal carcinoma is the most common histologic type found in all age groups. Grading is done according to Elston Ellis’s modification of the Scarff Bloom Richardson Scoring system. Hormonal {Estrogen Receptor (ER), Progesterone Receptor (PR)} and Human Epidermal growth factor Receptor-2 (HER2/neu) immunohistochemical expression are used for molecular typing of breast carcinoma. Young women account for up to 25% of all breast carcinoma cases in India. Aim: To assess the clinical symptoms, histological type, grade, Immunohistochemistry (IHC) profile (ER, PR, HER2neu), and their relationships in females below 40 years of age with breast carcinoma. Materials and Methods: An ambispective cross-sectional study with a total of 99 cases of breast carcinoma in young females were included in the study, which was conducted in the Department of Pathology at Sri Aurobindo Medical College and PG Institute in Indore, Madhya Pradesh, India, between April 2022 and March 2024. Biopsies, lumpectomy, and total or modified mastectomy specimens were fixed in formalin and paraffin embedded. Sections were stained with Haematoxylin and Eosin (H&E) and IHC markers ER, PR, and HER2neu using Estrogen Receptor Alpha Antibody (EP1), EP2, and EP3 antibodies, respectively, on a fully automated workstation. Carcinomas were classified according to the World Health Organisation (WHO) classification and graded based on Elston Ellis’s modification of the Scarff Bloom Richardson System Scoring. The proportion of tumour cells and intensity were studied, and evaluation was done with appropriate controls. Results: The most common symptom was a breast lump (84.84%), while Invasive breast cancer of {No Special Type (NST)} was the most common histologic type (96.96%) and histological Grade III was predominant (53.53%) in the majority of patients. ER, PR, and HER2neu were positive in 57.57%, 51.51%, and 33.33% of cases, respectively. HER2neu expression was negative in the majority of cases (66.66%). The most common molecular subtype was Luminal A (35.35%), followed by Triple-negative Breast Cancer (TNBC) (29.29%), Luminal B (22.22%), and the least common was the HER2neu enriched subtype (13.13%). Conclusion: Invasive breast carcinoma (NST) is the most common histologic type, with higher histologic grade and a greater number of triple-negative cases seen in young females (

Abstract Purpose To investigate the performance of histogram features of non-Gaussian diffusion metrics for diagnosing muscle invasion and histological grade in bladder cancer (BCa). Methods Patients were prospectively allocated to MR scanner1 (training cohort) or MR2 (testing cohort) for conventional diffusion-weighted imaging (DWIconv) and multi-b-value DWI. Metrics of continuous time random walk (CTRW), diffusion kurtosis imaging (DKI), fractional-order calculus (FROC), intravoxel incoherent motion (IVIM), and stretched exponential model (SEM) were simultaneously calculated using multi-b-value DWI. Whole-tumor histogram features were extracted from DWIconv and non-Gaussian diffusion metrics for logistic regression analysis to develop diffusion models diagnosing muscle invasion and histological grade. The models’ performances were quantified by area under the receiver operating characteristic curve (AUC). Results MR1 included 267 pathologically-confirmed BCa patients (median age, 67 years [IQR, 46–82], 222 men) and MR2 included 83 (median age, 65 years [IQR, 31–82], 73 men). For discriminating muscle invasion, CTRW achieved the highest testing AUC of 0.915, higher than DWIconv’s 0.805 (p = 0.014), and similar to the combined diffusion model’s AUC of 0.885 (p = 0.076). For differentiating histological grade of non-muscle-invasion bladder cancer, IVIM outperformed a testing AUC of 0.897, higher than DWIconv’s 0.694 (p = 0.020), and similar to the combined diffusion model’s AUC of 0.917 (p = 0.650). In both tasks, DKI, FROC, and SEM failed to show diagnostic superiority over DWIconv (p > 0.05). Conclusion CTRW and IVIM are two potential non-Gaussian diffusion models to improve the MRI application in assessing muscle invasion and histological grade of BCa, respectively. Critical relevance statement Our study validates non-Gaussian diffusion imaging as a reliable, non-invasive technique for early assessment of muscle invasion and histological grade in BCa, enhancing accuracy in diagnosis and improving MRI application in BCa diagnostic procedures. Key Points Muscular invasion largely determines bladder salvageability in bladder cancer patients. Evaluated non-Gaussian diffusion metrics surpassed DWIconv in BCa muscle invasion and histological grade diagnosis. Non-Gaussian diffusion imaging improved MRI application in preoperative diagnosis of BCa. Graphical Abstract

Abstract Background Circulating total insulin-like growth factor-I (IGF-I) is an established risk factor for prostate cancer. However, only a small proportion of circulating IGF-I is free or readily dissociable from IGF-binding proteins (its bioavailable form), and few studies have investigated the association of circulating free IGF-I with prostate cancer risk. Methods We analyzed data from 767 prostate cancer cases and 767 matched controls nested within the European Prospective Investigation into Cancer and Nutrition cohort, with an average of 14-years (interquartile range = 2.9) follow-up. Matching variables were study center, length of follow-up, age, and time of day and fasting duration at blood collection. Circulating free IGF-I concentration was measured in serum samples collected at recruitment visit (mean age 55 years old; standard deviation = 7.1) using an enzyme-linked immunosorbent assay (ELISA). Conditional logistic regressions were performed to examine the associations of free IGF-I with risk of prostate cancer overall and subdivided by time to diagnosis (≤ 14 and > 14 years), and tumor characteristics. Results Circulating free IGF-I concentrations (in fourths and as a continuous variable) were not associated with prostate cancer risk overall (odds ratio [OR] = 1.00 per 0.1 nmol/L increment, 95% CI: 0.99, 1.02) or by time to diagnosis, or with prostate cancer subtypes, including tumor stage and histological grade. Conclusions Estimated circulating free IGF-I was not associated with prostate cancer risk. Further research may consider other assay methods that estimate bioavailable IGF-I to provide more insight into the well-substantiated association between circulating total IGF-I and subsequent prostate cancer risk.

The histological grade of oral squamous cell carcinoma affects the prognosis. In the present study, we performed a radiomics analysis to extract features from 18F-FDG PET image data, created machine learning models from the features, and verified the accuracy of the prediction of the histological grade of oral squamous cell carcinoma. The subjects were 191 patients in whom an 18F-FDG-PET examination was performed preoperatively and a histopathological grade was confirmed after surgery, and their tumor sizes were sufficient for a radiomics analysis. These patients were split in a 70%/30% ratio for use as training data and testing data, respectively. We extracted 2993 radiomics features from the PET images of each patient. Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), Naïve Bayes (NB), and K-Nearest Neighbor (KNN) machine learning models were created. The areas under the curve obtained from receiver operating characteristic curves for the prediction of the histological grade of oral squamous cell carcinoma were 0.72, 0.71, 0.84, 0.74, and 0.73 for LR, SVM, RF, NB, and KNN, respectively. We confirmed that a PET radiomics analysis is useful for the preoperative prediction of the histological grade of oral squamous cell carcinoma. [ABSTRACT FROM AUTHOR]

Purpose: To investigate the performance of histogram features of non-Gaussian diffusion metrics for diagnosing muscle invasion and histological grade in bladder cancer (BCa). Methods: Patients were prospectively allocated to MR scanner1 (training cohort) or MR2 (testing cohort) for conventional diffusion-weighted imaging (DWIconv) and multi-b-value DWI. Metrics of continuous time random walk (CTRW), diffusion kurtosis imaging (DKI), fractional-order calculus (FROC), intravoxel incoherent motion (IVIM), and stretched exponential model (SEM) were simultaneously calculated using multi-b-value DWI. Whole-tumor histogram features were extracted from DWIconv and non-Gaussian diffusion metrics for logistic regression analysis to develop diffusion models diagnosing muscle invasion and histological grade. The models' performances were quantified by area under the receiver operating characteristic curve (AUC). Results: MR1 included 267 pathologically-confirmed BCa patients (median age, 67 years [IQR, 46–82], 222 men) and MR2 included 83 (median age, 65 years [IQR, 31–82], 73 men). For discriminating muscle invasion, CTRW achieved the highest testing AUC of 0.915, higher than DWIconv's 0.805 (p = 0.014), and similar to the combined diffusion model's AUC of 0.885 (p = 0.076). For differentiating histological grade of non-muscle-invasion bladder cancer, IVIM outperformed a testing AUC of 0.897, higher than DWIconv's 0.694 (p = 0.020), and similar to the combined diffusion model's AUC of 0.917 (p = 0.650). In both tasks, DKI, FROC, and SEM failed to show diagnostic superiority over DWIconv (p > 0.05). Conclusion: CTRW and IVIM are two potential non-Gaussian diffusion models to improve the MRI application in assessing muscle invasion and histological grade of BCa, respectively. Critical relevance statement: Our study validates non-Gaussian diffusion imaging as a reliable, non-invasive technique for early assessment of muscle invasion and histological grade in BCa, enhancing accuracy in diagnosis and improving MRI application in BCa diagnostic procedures. Key Points: Muscular invasion largely determines bladder salvageability in bladder cancer patients. Evaluated non-Gaussian diffusion metrics surpassed DWIconv in BCa muscle invasion and histological grade diagnosis. Non-Gaussian diffusion imaging improved MRI application in preoperative diagnosis of BCa. [ABSTRACT FROM AUTHOR]

Rationale and Objectives: We aimed to develop and validate prediction models for histological grade of invasive breast carcinoma (BC) based on ultrasound radiomics features and clinical characteristics. Materials and Methods: A number of 383 patients with invasive BC were retrospectively enrolled and divided into a training set (207 patients), internal validation set (90 patients), and external validation set (86 patients). Ultrasound radiomics features were extracted from all the eligible patients. The Boruta method was used to identify the most useful features. Seven classifiers were adopted to developed prediction models. The output of the classifier with best performance was labeled as the radiomics score (Rad-score) and the classifier was selected as the Rad-score model. A combined model combining clinical factors and Rad-score was developed. The performance of the models was evaluated using receiver operating characteristic curve. Results: Seven radiomics features were selected from 788 candidate features. The logistic regression model performing best among the 7 classifiers in the internal and external validation sets was considered as Rad-score model, with areas under the receiver operating characteristic curve (AUC) values of 0.731 and 0.738. The tumor size was screened out as the risk factor and the combined model was developed, with AUC values of 0.721 and 0.737 in the internal and external validation sets. Furthermore, the 10-fold cross-validation demonstrated that the 2 models above were reliable and stable. Conclusion: The Rad-score model and combined model were able to predict histological grade of invasive BC, which may enable tailored therapeutic strategies for patients with BC in routine clinical use. [ABSTRACT FROM AUTHOR]

Objective To explore the predictive value of different machine learning models based on MRI radiomics combined with clinical features for histological grade of cervical squamous cell carcinoma. Methods Clinical data of 150 patients with cervical squamous cell carcinoma confirmed by pathological biopsy were retrospectively analyzed. They were randomly divided into the training set and validation set at a ratio of 4∶1. Features were extracted from the regions of interest of T2WI fat suppression sequence （FS-T2WI） and enhanced T1WI （delayed phase）. After dimensionality reduction and feature selection， logistic regression （LR）， support vector machine （SVM）， naïve Bayes （NB）， random forest （RF）， Light Gradient Boosting Machine （LightGBM）， K-nearest neighbor （KNN） were used to construct a radiomics model for predicting the histological grade of cervical squamous cell carcinoma. The area under the receiver operating characteristic （ROC） curve （AUC） was used to evaluate the predictive performance of the six models. Univariate and multivariate logistic regression analyses were performed to predict the independent risk factors， and a combined model of clinical and radiomics was established. The differences of each model were compared by AUC， and the clinical value of the model was evaluated by decision curve （DCA）. Results In the radiomics model， the LightGBM model had the largest AUC （0.910 in the training set， and 0.839 in the validation set）. The AUC of clinical features combined with LightGBM model was the largest （0.935 in the training set， and 0.888 in the validation set）， which was higher than those of clinical model （0.762 in the training set， and 0.710 in the validation set） and LightGBM radiomics model. Conclusions The LightGBM model has a high predictive value in the radiomics model. The combined model has the optimal DCA effect and the highest clinical net benefit. The combined prediction model combining radiomics and clinical features has good predictive value for cervical squamous cell carcinoma with low differentiation， providing a non-invasive and efficient method for clinical decision-making.

Abstract Background The histological grade is an important factor in the prognosis of invasive breast cancer and is vital to accurately identify the histological grade and reclassify of Grade2 status in breast cancer patients. Methods In this study, data were collected from 556 invasive breast cancer patients, and then randomly divided into training cohort (n = 335) and validation cohort (n = 221). All patients were divided into actual low risk group (Grade1) and high risk group (Grade2/3) based on traditional histological grade, and tumor-infiltrating lymphocyte score (TILs-score) obtained from multiphoton images, and the TILs assessment method proposed by International Immuno-Oncology Biomarker Working Group (TILs-WG) were also used to differentiate between high risk group and low risk group of histological grade in patients with invasive breast cancer. Furthermore, TILs-score was used to reclassify Grade2 (G2) into G2 /Low risk and G2/High risk. The coefficients for each TILs in the training cohort were retrieved using ridge regression and TILs-score was created based on the coefficients of the three kinds of TILs. Results Statistical analysis shows that TILs-score is significantly correlated with histological grade, and is an independent predictor of histological grade (odds ratio [OR], 2.548; 95%CI, 1.648–3.941; P 0.05 in the univariate analysis). Moreover, the risk of G2/High risk group is higher than that of G2/Low risk group, and the survival rate of patients with G2/Low risk is similar to that of Grade1, while the survival rate of patients with G2/High risk is even worse than that of patients with G3. Conclusion Our results suggest that TILs-score can be used to predict the histological grade of breast cancer and potentially to guide the therapeutic management of breast cancer patients.

Qin Ma,1 Yao-Bang Liu,2 Tong She,3 Xin-Lan Liu4 1Department of Radiation Oncology, General Hospital of Ningxia Medical University, Yinchuan, Ningxia, 750004, People’s Republic of China; 2Department of Surgical Oncology, General Hospital of Ningxia Medical University, Yinchuan, Ningxia, 750004, People’s Republic of China; 3Hospital of Zhongwei, Zhongwei, People’s Republic of China; 4Department of Medical Oncology, General Hospital of Ningxia Medical University, Yinchuan, Ningxia, 750004, People’s Republic of ChinaCorrespondence: Xin-Lan Liu, Tel +8613709577339, Email nxliuxinlan@126.comObjective: This study determined the cut-off value of Ki-67 expression and discussed the interaction between Ki-67 and histological grade, further explored the prognostic role of Ki-67 in hormone receptor-positive and human epidermal growth factor receptor 2 negative (HR+/HER2-) breast cancer;.Materials and Methods: We assessed the Ki-67 expression of 956 patients with HR+/HER2 breast cancer diagnosed in the General Hospital of Ningxia Medical University from 2015 to 2019 by immunohistochemistry (IHC), The disease-free survival (DFS) was defined as the time from postoperative to the first local recurrence, distant metastasis or death of the disease. The follow-up by means of inpatient or outpatient medical records and telephone.Results: 22.5% was used as the cut-off for low/high Ki-67 expression in HR+/HER2- breast cancer. Compared with the value of 14%, which is commonly used in clinic at present, the consistency of the two values is moderate (Kappa = 0.484, P< 0.001). The expression of Ki-67 was increased with the grade. (Median: G1:10%; G2:20%; G3:40%. Mean: G1:13%; G2:23%; G3:39%, P < 0.001). Survival analysis was based on all patients for a median of 51 months (24– 89 months), 63 cases had recurrence or metastasis during the follow-up, which 21 cases had low expression of Ki-67 and 42 cases had high expression. The patients with Ki-67 ≥ 22.5% had a 2.969 higher risk of early recurrence and metastasis than the patients with Ki-67 < 22.5%. There were 4 cases of local recurrence, 7 cases of regional lymph node metastasis, and 52 cases of distant metastasis in all patients, the common distant metastases were bone, liver, and lung, and rare metastases were adrenal gland, bone marrow, and pericardium.Conclusion: In HR+/HER2- breast cancer, patients with Ki-67 > 22.5% have a worse prognosis and are more likely to have early recurrence and metastasis.Keywords: Ki-67, breast cancer, cut-off value, histological grade, recurrence and metastasis

Although BRCA genes are well-known breast cancer genes, the clinicopathological features of breast cancer patients carrying BRCA1/2 pathogenic variants have not been adequately defined. The goals of this study were to determine the distribution of BRCA1/2 variants in the Turkish population and its correlation with clinicopathological features. Clinical data of 151 women who underwent BRCA1/2 gene testing at Mersin University Medical Faculty Hospital between 2016 and 2019 were retrospectively analyzed. BRCA1/2 variants were detected as pathogenic (n = 11), variants of uncertain significance (n = 5), likely benign (n = 3), and benign (n = 81) in breast cancer cases. The BRCA1/2 pathogenic variant carriers had a higher histological grade, rate of triple- negative type, Ki-67 proliferation index, and rate of no special type carcinoma than the group without mutation (p = 0.03, 0.01, 0.04, and 0.02 respectively). We analyzed the distribution of variants we detected in women living in our region and found that pathogenic variants in patients with breast cancer were associated with high histological grade, triple-negative type, high Ki-67 proliferation index, and histological type. Studies in diverse populations are needed to establish a clinicopathological relationship with variants more easily.

Methods: A total of 183 patients with pathologically confirmed breast cancer were included. Contrast enhancement patterns and quantitative parameters were compared in different groups. The receiver operating characteristic (ROC) curve was used to analyze the efficacy of CEUS in the preoperative prediction of pathological characteristics, including histologic grade and molecular subtypes. Results: Heterogeneous enhancement, perfusion defects, and peripheral radial vessels were mostly observed in higher histologic grade (grade III) breast cancer. Heterogeneous enhancement and perfusion defect were the most effective indicators for grade III breast cancer, with the areas under the ROC curve of 0.768 and 0.756, respectively. There were significant differences in the enhancement intensity, post-enhanced margin, perfusion defects, and peripheral radial vessel among the different molecular subtypes of breast cancer (all P < 0.01). Perfusion defects and clear edge after enhancement were the best qualitative criteria for the diagnosis of HER-2 overexpressed and triple-negative breast cancers, and the corresponding areas under the ROC curves were 0.804 and 0.905, respectively. There were significant differences in PE, WiR, WiPI, and WiWoAUC between grade III vs grade I and II breast cancer (P < 0.05). PE, WiR, WiPI, and WiWoAUC had good efficiency in the diagnosis of high-histologic-grade breast cancer. PE had the highest diagnostic efficiency in Luminal A, while WiPI had the highest diagnostic efficiency in Luminal B subtype breast cancer, and the areas under the ROC curve were 0.825 and 0.838, respectively. WiWoAUC and WiR were the most accurate parameters for assessing triple-negative subtype breast cancers, and the areas under the curve were 0.932 and 0.922, respectively. Conclusion: Qualitative and quantitative perfusion analysis of contrastenhanced ultrasound may be useful in the non-invasive prediction of the histological grade and molecular subtypes of breast cancers. [ABSTRACT FROM AUTHOR]

Background: The histological grade is an important factor in the prognosis of invasive breast cancer and is vital to accurately identify the histological grade and reclassify of Grade2 status in breast cancer patients. Methods: In this study, data were collected from 556 invasive breast cancer patients, and then randomly divided into training cohort (n = 335) and validation cohort (n = 221). All patients were divided into actual low risk group (Grade1) and high risk group (Grade2/3) based on traditional histological grade, and tumor-infiltrating lymphocyte score (TILs-score) obtained from multiphoton images, and the TILs assessment method proposed by International Immuno-Oncology Biomarker Working Group (TILs-WG) were also used to differentiate between high risk group and low risk group of histological grade in patients with invasive breast cancer. Furthermore, TILs-score was used to reclassify Grade2 (G2) into G2 /Low risk and G2/High risk. The coefficients for each TILs in the training cohort were retrieved using ridge regression and TILs-score was created based on the coefficients of the three kinds of TILs. Results: Statistical analysis shows that TILs-score is significantly correlated with histological grade, and is an independent predictor of histological grade (odds ratio [OR], 2.548; 95%CI, 1.648–3.941; P < 0.0001), but TILs-WG is not an independent predictive factor for grade (P > 0.05 in the univariate analysis). Moreover, the risk of G2/High risk group is higher than that of G2/Low risk group, and the survival rate of patients with G2/Low risk is similar to that of Grade1, while the survival rate of patients with G2/High risk is even worse than that of patients with G3. Conclusion: Our results suggest that TILs-score can be used to predict the histological grade of breast cancer and potentially to guide the therapeutic management of breast cancer patients. [ABSTRACT FROM AUTHOR]

Given the widespread use of whole slide imaging (WSI) for primary pathological diagnosis, we evaluated its utility in assessing histological grade and biomarker expression (ER, PR, HER2, and Ki67) compared to conventional light microscopy (CLM). In addition, we explored the utility of digital image analysis (DIA) for assessing biomarker expression. Three breast pathologists assessed the Nottingham combined histological grade, its components, and biomarker expression through the immunohistochemistry of core needle biopsy samples obtained from 101 patients with breast cancer using CLM, WSI, and DIA. There was no significant difference in variance between the WSI and CLM agreement rates for the Nottingham grade and its components and biomarker expression. Nuclear pleomorphism emerged as the most variable histologic component in intra- and inter-observer agreement (kappa ≤ 0.577 and kappa ≤ 0.394, respectively). The assessment of biomarker expression using DIA achieved an enhanced kappa compared to the inter-observer agreement. Compared to each observer's assessment, DIA exhibited an improved kappa coefficient for the expression of most biomarkers with CLM and WSI. Using WSI to assess prognostic and predictive factors, including histological grade and biomarker expression in breast cancer, is acceptable. Furthermore, incorporating DIA to assess biomarker expression shows promise for substantially enhancing scoring reproducibility. [ABSTRACT FROM AUTHOR]

Objective To explore the predictive value of different machine learning models based on MRI radiomics combined with clinical features for histological grade of cervical squamous cell carcinoma. Methods Clinical data of 150 patients with cervical squamous cell carcinoma confirmed by pathological biopsy were retrospectively analyzed. They were randomly divided into the training set and validation set at a ratio of 4 ∶ 1. Features were extracted from the regions of interest of T2WI fat suppression sequence (FS-T2WI) and enhanced T1WI (delayed phase). After dimensionality reduction and feature selection, logistic regression (LR), support vector machine (SVM), naïve Bayes (NB), random forest (RF), Light Gradient Boosting Machine (LightGBM), K-nearest neighbor (KNN) were used to construct a radiomics model for predicting the histological grade of cervical squamous cell carcinoma. The area under the receiver operating characteristic (ROC) curve (AUC) was used to evaluate the predictive performance of the six models. Univariate and multivariate logistic regression analyses were performed to predict the independent risk factors, and a combined model of clinical and radiomics was established. The differences of each model were compared by AUC, and the clinical value of the model was evaluated by decision curve (DCA). Results In the radiomics model, the LightGBM model had the largest AUC (0.910 in the training set, and 0.839 in the validation set). The AUC of clinical features combined with LightGBM model was the largest (0.935 in the training set, and 0.888 in the validation set), which was higher than those of clinical model (0.762 in the training set, and 0.710 in the validation set) and LightGBM radiomics model. Conclusions The LightGBM model has a high predictive value in the radiomics model. The combined model has the optimal DCA effect and the highest clinical net benefit. The combined prediction model combining radiomics and clinical features has good predictive value for cervical squamous cell carcinoma with low differentiation, providing a noninvasive and efficient method for clinical decision-making. [ABSTRACT FROM AUTHOR]

Oral squamous cell carcinoma (OSCC) is a common and highly aggressive dog tumor known for its local invasiveness and metastatic potential. Understanding the molecular mechanisms driving the development and progression of OSCC is crucial for improving diagnostic and therapeutic strategies. Additionally, spontaneous oral squamous cell carcinomas in dogs are an excellent model for studying human counterparts. In this study, we aimed to investigate the significance of two key molecular components, Cox-2 and EGFR, in canine OSCC. We examined 34 tumor sections from various dog breeds to assess the immunoexpression of Cox-2 and EGFR. Our findings revealed that Cox-2 was highly expressed in 70.6% of cases, while EGFR overexpression was observed in 44.1%. Cox-2 overexpression showed association with histological grade of malignancy (HGM) (p = 0.006) and EGFR with vascular invasion (p = 0.006). COX-2 and EGFR concurrent expression was associated with HGM (p = 0.002), as well as with the presence of vascular invasion (p = 0.002). These data suggest that Cox-2 and EGFR could be promising biomarkers and potential therapeutic targets, opening avenues for developing novel treatment strategies for dogs affected by OSCC. Further studies are warranted to delve deeper into these findings and translate them into clinical practice.

This study aimed to identify microRNAs associated with histological grade using comprehensive microRNA analysis data obtained by next-generation sequencing from early-stage invasive breast cancer. RNA-seq data from normal breast and breast cancer samples were compared to identify candidate microRNAs with differential expression using bioinformatics. A total of 108 microRNAs were significantly differentially expressed in normal breast and breast cancer tissues. Using clinicopathological information and microRNA sequencing data of 430 patients with breast cancer from The Cancer Genome Atlas (TCGA), the differences in candidate microRNAs between low- and high-grade tumors were identified. Comparing the expression of the 108 microRNAs between low- and high-grade cases, 25 and 18 microRNAs were significantly upregulated and downregulated, respectively, in high-grade cases. Clustering analysis of the TCGA cohort using these 43 microRNAs identified two groups strongly predictive of histological grade. miR-3677 is a microRNA upregulated in high-grade breast cancer. The outcome analysis revealed that patients with high miR-3677 expression had significantly worse prognosis than those with low miR-3677 expression. This study shows that microRNAs are associated with histological grade in early-stage invasive breast cancer. These findings contribute to the elucidation of a new mechanism of breast cancer growth regulated by specific microRNAs. [ABSTRACT FROM AUTHOR]

Although BRCA genes are well-known breast cancer genes, the clinicopathological features of breast cancer patients carrying BRCA1/2 pathogenic variants have not been adequately defined. The goals of this study were to determine the distribution of BRCA1/2 variants in the Turkish population and its correlation with clinicopathological features. Clinical data of 151 women who underwent BRCA1/2 gene testing at Mersin University Medical Faculty Hospital between 2016 and 2019 were retrospectively analyzed. BRCA1/2 variants were detected as pathogenic (n = 11), variants of uncertain significance (n = 5), likely benign (n = 3), and benign (n = 81) in breast cancer cases. The BRCA1/2 pathogenic variant carriers had a higher histological grade, rate of triple-negative type, Ki-67 proliferation index, and rate of no special type carcinoma than the group without mutation (p = 0.03, 0.01, 0.04, and 0.02 respectively). We analyzed the distribution of variants we detected in women living in our region and found that pathogenic variants in patients with breast cancer were associated with high histological grade, triple-negative type, high Ki-67 proliferation index, and histological type. Studies in diverse populations are needed to establish a clinicopathological relationship with variants more easily. [ABSTRACT FROM AUTHOR]

Simple Summary: Abdominal ultrasound (US) is a widely used, first-level technique for the diagnosis of hepatocellular carcinoma (HCC). However, the prognostic meaning of different US patterns of HCC has not been investigated yet. In this study, we present a novel four-type US classification based on HCC morphology; we aimed to assess the impact of US patterns on overall survival (OS) and recurrence-free survival (RFS) in a cohort of HCC patients treated with radiofrequency thermal ablation and with a long-term follow-up. We demonstrated that specific US patterns are independent predictors of both OS and RFS and, therefore, could help identify patients at a higher risk of worse prognosis in clinical practice. Background: Hepatocellular carcinoma (HCC) is a leading cause of cancer-related death. Abdominal ultrasound (US) is by far the most widely used first-level exam for the diagnosis of HCC. We aimed to assess whether different ultrasound patterns were related to tumor prognosis. Methods: We retrospectively reviewed all patients with a new diagnosis of HCC (single nodule) and undergoing radiofrequency thermal ablation (RFTA) at our clinic between January 2009 and December 2021. Patients were classified according to four HCC ultrasound patterns: 1A, single capsulated nodule; 1B, well capsulated intra-node nodule; 1C, cluster consisting of capsulated nodules; and 2, non-capsulated nodule. Results: 149 patients were analysed; median follow-up time was 43 months. US patterns 1A (32.9%) and 1B (61.1%) were the most commonly seen. Median overall survival (OS) and recurrence-free survival (RFS) from RFTA were 54 months (95% CI, 42–66) and 22 months (95% CI, 12–32), respectively. Pattern 1A showed the best OS. Compared to pattern 1A, 1B was independently associated with worse OS (51 months (95% CI, 34–68) vs. 46 months (95% CI, 18–62)) and RFS (34 months (95% CI, 27–41) vs. 18 months (95% CI, 12–24)). Patterns 1C and 2 were associated with worse RFS compared to 1A, while no difference was seen for OS. Among baseline clinical variables, pattern 1B exhibited higher histological grade (p = 0.048) and tumor dimension (p = 0.034) compared to pattern 1A. Conclusions: Our findings demonstrate that different US patterns correlate with different survival outcomes and tumor behavior in patients with HCC. Prospective studies are needed to confirm these results. [ABSTRACT FROM AUTHOR]

Yitian Wu,1 Weixing Pan,2 Lingxia Wang,3 Wenting Pan,2 Huangqi Zhang,2 Shengze Jin,1 Xiuli Wu,4 Aie Liu,5 Enhui Xin,5 Wenbin Ji6,7 1School of Medicine, Shaoxing University, Shaoxing, Zhejiang, 312000, People’s Republic of China; 2Department of Radiology, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Linhai, Zhejiang, 317000, People’s Republic of China; 3Department of Radiology, Taizhou Hospital, Zhejiang University, Taizhou, Zhejiang, 317000, People’s Republic of China; 4Department of Nuclear Medicine, Taizhou Hospital of Zhejiang Province, Linhai, Zhejiang, 317000, People’s Republic of China; 5Department of Research Center, Shanghai United Imaging Intelligence Co., Ltd, Shanghai, 200000, People’s Republic of China; 6Department of Radiology, Taizhou Hospital of Zhejiang Province, Shaoxing University, Taizhou, Zhejiang, 312000, People’s Republic of China; 7Key Laboratory of Evidence-Based Radiology of Taizhou, Linhai, Zhejiang, 317000, People’s Republic of ChinaCorrespondence: Wenbin Ji, Email wb.j@163.comBackground: Histological grade is an important prognostic factor for patients with breast cancer and can affect clinical decision-making. From a clinical perspective, developing an efficient and non-invasive method for evaluating histological grading is desirable, facilitating improved clinical decision-making by physicians. This study aimed to develop an integrated model based on radiomics and clinical imaging features for preoperative prediction of histological grade invasive breast cancer.Methods: In this retrospective study, we recruited 211 patients with invasive breast cancer and randomly assigned them to either a training group (n=147) or a validation group (n=64) with a 7:3 ratio. Patients were classified as having low-grade tumors, which included grade I and II tumors, or high-grade tumors, which included grade III tumors. Three models were constructed based on basic clinical features, radiomics features, and the sum of the two. To assess diagnostic performance of the radiomics models, we employed measures such as receiver operating characteristic (ROC) curve, decision curve analysis (DCA), accuracy, sensitivity, and specificity, and the predictive performance of the three models was compared using the DeLong test and net reclassification improvement (NRI).Results: The area under the curve (AUC) of the clinical model, radiomics model, and comprehensive model was 0.682, 0.833, and 0.882 in the training set and 0.741, 0.751, and 0.836 in the validation set, respectively. NRI analysis confirmed that the combined model was better than the other two models in predicting the histological grade of breast cancer (NRI=21.4% in the testing cohort).Conclusion: Compared with the other models, the comprehensive model based on the combination of basic clinical features and radiomics features exhibits more significant potential for predicting histological grade and can better assist clinicians in optimal decision-making.Keywords: breast cancer, histological grade, magnetic resonance imaging, radiomics, comprehensive model, NRI

The histological grade of oral squamous cell carcinoma affects the prognosis. In the present study, we performed a radiomics analysis to extract features from 18F-FDG PET image data, created machine learning models from the features, and verified the accuracy of the prediction of the histological grade of oral squamous cell carcinoma. The subjects were 191 patients in whom an 18F-FDG-PET examination was performed preoperatively and a histopathological grade was confirmed after surgery, and their tumor sizes were sufficient for a radiomics analysis. These patients were split in a 70%/30% ratio for use as training data and testing data, respectively. We extracted 2993 radiomics features from the PET images of each patient. Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), Naïve Bayes (NB), and K-Nearest Neighbor (KNN) machine learning models were created. The areas under the curve obtained from receiver operating characteristic curves for the prediction of the histological grade of oral squamous cell carcinoma were 0.72, 0.71, 0.84, 0.74, and 0.73 for LR, SVM, RF, NB, and KNN, respectively. We confirmed that a PET radiomics analysis is useful for the preoperative prediction of the histological grade of oral squamous cell carcinoma.

Abstract Background Carbohydrate antigen (CA) 19‐9 and histological grade can serve as indicators of the biological characteristics of pancreatic ductal adenocarcinoma (PDAC). Aims The aim of this study was to investigate the combined impact of preoperative CA19‐9 levels and histological grade on prognosis and classification accuracy in PDAC patients. Methods and results A retrospective cohort study was conducted on 612 patients with PDAC who underwent curative pancreatectomy, and a biological risk model based on preoperative CA19‐9 levels and histology grade was established. The prognostic importance of the biological risk model was evaluated, and its validity was confirmed through a validation cohort of 218 patients. The survival of patients with PDAC was independently associated with preoperative CA19‐9 levels and histology grade, indicating a biological risk. This biological risk was incorporated into the eighth edition of the TNM staging system, leading to the development of a modified TNM (mTNM) staging system. Receiver operating characteristic (ROC) curves demonstrated that the mTNM staging system had a significantly larger area under the curve (AUC) than the TNM staging system. The discriminatory capacity of the mTNM staging system was further validated in an independent cohort. Conclusion Biological risk based on preoperative CA19‐9 and histological grade could predict the survival of patients with PDAC. The incorporation of biological risk into the TNM staging system has the potential to enhance the accuracy of patient classification in PDAC, predicting patient survival and enabling the development of individualized treatment plans.

Simple Summary: TLR-9, a vital component of our immune system, is found to be significantly involved in the progression of gastric cancer. This research also investigates its connection to EBV infection. By analyzing specific T and B immune cell groups that express TLR-9, along with the levels of soluble TLR-9 in the blood, the study compares these factors between individuals with gastric cancer and healthy volunteers. The aim is to understand how these immune markers relate to the severity of the cancer, its stage, type, and survival rates. Furthermore, the study delves into the presence of EBV genetic material and its potential impact on these immune responses. This research sheds light on the interplay between TLR-9, immune cell populations, and EBV in gastric cancer development. The relationship between Toll-like receptor 9 (TLR-9) signaling and its involvement with Epstein–Barr virus (EBV) in gastric cancer (GC) is complex and currently under study. This research intended to understand TLR-9's role in certain T and B lymphocytes and the serum levels of TLR-9 in GC patients versus healthy subjects. The team explored links between these immune markers and various GC traits, such as histological grade, tumor progression stages, cancer types, and survival rates. Additionally, the research sought to find if EBV genetic material influences these immune reactions. Using flow cytometry, TLR-9 levels in different immune cells were analyzed. At the same time, the amount of TLR-9 in the serum was determined. The results showed GC patients had varied TLR-9 levels compared to healthy subjects, with specific cells showing noticeable changes. When grouped by GC attributes, key relationships emerged between TLR-9 amounts, the histological grade, progression stages, and cancer types. A notable finding was the connection between TLR-9 levels and EBV genetic presence, suggesting possible interactions between TLR-9 responses and EBV-related GC processes. Survival data also hinted at TLR-9's potential as a predictor linked to clinical traits. Overall, this research emphasizes TLR-9's complex role in GC's immune responses, pinpointing its interactions with particular cells, clinical features, and EBV. The study unveils a complex web affecting GC and paves the way for new treatment avenues targeting TLR-9 pathways. [ABSTRACT FROM AUTHOR]

Objectives: Tumor grading is important for prognosis of pancreatic ductal adenocarcinoma (PDAC). In this study, we developed preoperative clinical-radiomics nomograms using features from contrast-enhanced CT (CECT), to discriminate high-grade and low-grade PDAC and predict overall survival (OS). Methods: In this single-center, retrospective study conducted from February 2014 to April 2021, consecutive PDAC patients who underwent CECT and had pathologically identified grading were randomized to training (n=200) and test (n=84) cohorts for development of model to predict histological grade based on radiomics scores from CECT (HGrad). Another 42 patients were used as external validation cohort of HGrad. A nomogram (HGnom) was constructed using radiomics score, CA12-5 and smoking to predict histological grade. A second nomogram (Pnom) was constructed using radiomics score, CA12-5, TNM, adjuvant treatment, resection margin and microvascular invasion to predict OS in radical resection patients (217 of 284). Results: Among 326 patients, 122 were high-grade (120 poorly differentiated and 2 undifferentiated). The HGrad yielded AUCs of 0.75 (95% CI: 0.64, 0.85) and 0.76 (95% CI: 0.60, 0.91) in test and validation cohorts. The HGnom achieved AUCs of 0.77 (95% CI: 0.66, 0.87), and the predicted grades calibrated well with actual grades (P=.13). OS was different between the grades predicted by radiomics scores (P=.01). The integrated AUC of the Pnom for predicting OS was 0.80 (95% CI: 0.75, 0.88). Conclusion: Compared with the HGrad using features from CECT, the HGnom demonstrated higher performance for predicting histological grade. The Pnom helped identify patients with high survival outcome in pancreatic ductal adenocarcinoma. [ABSTRACT FROM AUTHOR]

Aims: The prognostic role of EGFR mutations remains controversial. We aimed to evaluate the prognostic role of EGFR mutation in consideration of the IASLC histological grade in patients with resected early‐stage lung adenocarcinoma. Methods and results: A total of 3297 patients with stages I–IIA resected lung adenocarcinoma who had had EGFR mutation tests between January 2014 and December 2019 at the Samsung Medical Center, Seoul, Korea were included. Recurrence‐free survival (RFS) was compared by EGFR mutation status (EGFR‐M+ versus EGFR‐WT) and IASLC histological grade (G1, G2 and G3). Cox proportional hazards models were used to estimate the adjusted HRs (aHRs) and 95% confidence intervals (CIs). Results: Compared to the EGFR‐WT group, the EGFR‐M+ group had a significantly lower proportion of G3 tumour (16 versus 33%, P < 0.001). During a median follow‐up of 41.4 months, 376 patients experienced recurrence. After adjusting for histological grade, the aHR for recurrence comparing the EGFR‐M+ to the EGFR‐WT was 1.30 (95% CI = 1.04–1.62, P = 0.022). The EGFR‐M+ group had a significantly lower 5‐year RFS than the EGFR‐WT group among G3 patients (58.4 versus 71.5%, P < 0.001), but not among G1 and G2 patients. Conclusions: EGFR mutation status was associated with a risk of recurrence after consideration of the IASLC histological grading, especially in G3 tumours. The results of this study would be useful for developing a new staging system and identifying a subset of patients who may benefit from adjuvant targeted therapy. [ABSTRACT FROM AUTHOR]

Background: Radiomics‐based preoperative evaluation of lymph node metastasis (LNM) and histological grade (HG) might facilitate the decision‐making for pancreatic cancer and further efforts are needed to develop effective models. Purpose: To develop multiparametric MRI (MP‐MRI)‐based radiomics models to evaluate LNM and HG. Study Type: Retrospective. Population: The pancreatic cancer patients from the main center (n = 126) were assigned to the training and validation sets at a 4:1 ratio. The patients from the other center (n = 40) served as external test sets. Field Strength/Sequence: A 3.0 T and 1.5 T/T2‐weighted imaging, diffusion‐weighted imaging, and dynamic contrast enhancement T1‐weighted imaging. Assessment: A total of 10,686 peritumoral and intratumoral radiomics features were extracted which contained first‐order, shape‐based, and texture features. The following three‐step method was applied to reduce the feature dimensionality: SelectKBest (a function from scikit‐learn package), least absolute shrinkage and selection operator (LASSO), and recursive feature elimination based on random forest (RFE‐RF). Six classifiers (random forest, logistic regression, support vector machine, K‐nearest neighbor, decision tree, and XGBOOST) were trained and selected based on their performance to construct the clinical, radiomics, and combination models. Statistical Tests: Delong's test was used to compare the models' performance. P value less than 0.05 was considered significant. Results: Twelve significant features for LNM and 11 features for HG were obtained. Random forest and logistic regression performed better than the other classifiers in evaluating LNM and HG, respectively, according to the surgical pathological results. The best performance was obtained with the models that combined peritumoral and intratumoral features with area under curve (AUC) values of 0.944 and 0.892 in the validation and external test sets for HG and 0.924 and 0.875 for LNM. Data Conclusion: Radiomics holds the potential to evaluate LNM and HG of pancreatic cancer. The combination of peritumoral and intratumoral features will make models more accurate. Evidence Level: 4. Technical Efficacy: Stage 2. [ABSTRACT FROM AUTHOR]

Background/Aim: Endometrial carcinoma (EC) is the most common gynecological cancer, but lacks specific targetable markers. In order to explore the immune-related molecules that affect the progression and prognosis of EC, we analyzed the differential expression of genes in different histological grades of the disease. Materials and Methods: EC-related gene-expression data of different histological grades were downloaded from TCGA and GEO databases. The list of immune-related genes was obtained from the ImmPort database. In order to identity differentiallyexpressed genes (DEGs), differential-expression analysis was performed. The intersection of DEGs and immunerelated genes was termed immune-related differentiallyexpressed genes (IRDEGs). IRDEGs were enriched in cancer-related functional pathways by gene-correlation analysis and GSEA-enrichment analysis. The association of IRDEGs with immune-cell tumor infiltration and gene polymorphisms was analyzed using IRDEG mRNA and protein-expression data in EC from TCGA and THPA databases. Results: Three IRDEGs, TNFSF15, SEMA3E and TNFSF10, were involved in the analysis of the prognosis of EC patients. IRDEGs were not only related to clinical characteristics but could also affect the prognosis of patients. Gene-correlation and GSEA-enrichment analysis of IRDEGs showed that TNFSF15 and TNFSF10 were coenriched in the IL2-STAT5 functional pathway. IRDEGs had a significant correlation with a variety of immune-cell types infiltrating EC tumors and were related to EC prognosis. IRDEG mRNA- and protein-expression levels were increased in EC compared to normal tissues. Conclusion: TNFSF15, SEMA3E and TNFSF10 may regulate the progression and prognosis of EC patients by affecting immune-cell infiltration of EC tumors. [ABSTRACT FROM AUTHOR]

Abstract Background Histological grade has been demonstrated to be an important factor of breast cancer outcome and is associated with cell differentiation and is currently being evaluated via H&E-stained sections. Molecular biomarkers are essential to improve the accuracy of histological grading. ATBF1, a large transcription factor, has been considered a tumor suppressor gene with frequent mutations or deletions in multiple cancers. In breast cancer, ATBF1 was reported to function in cell differentiation and mammary development. However, its role in the clinic has rarely been reported. Methods Breast cancer tissues (BCTs) and adjacent noncancerous tissues (ANCTs) were collected to analyze the expression of ATBF1 at the mRNA and protein levels. Three anti-ATBF1 antibodies recognizing independent peptides of ATBF1 (N-terminal end, middle region and C-terminal end) were applied for IHC staining. Small interfering RNA (siRNA) was used to silence ATBF1 expression and to investigate the roles of ATBF1 in MCF7 cells. Microarrays were introduced to analyze the differentially expressed genes, enriched GO terms and KEGG terms regulated by ATBF1 and its potential downstream genes, which were further confirmed in vitro and in clinical samples. Results The expression of ATBF1 was reduced in BCTs at both the mRNA and protein levels compared with that in ANCTs. ATBF1 protein was predominantly localized in the nucleus of ANCTs but in the cytoplasm of BCTs. Both the mRNA and protein levels of ATBF1 were significantly correlated with histological grade. Consistently, knockdown of ATBF1 increased stemness marker expression and reduced differentiation markers in vitro. Further analysis identified WNT5A as an essential downstream gene of ATBF1 in breast cancer cells. Treatment of WNT5A disrupted cell proliferation induced by ATBF1 silencing. In BCTs, a significant correlation was observed between the expression of WNT5A and ATBF1. Conclusion The results indicated that ATBF1 expression might be a useful diagnostic marker associated with histological grade and breast cancer malignancy. WNT5A and its signaling pathway are novel mechanisms by which ATBF1 contributes to breast cancer tumorigenesis.

Abstract Background To predict the histological grade and microvascular invasion (MVI) in patients with HCC. Methods A retrospective analysis was conducted on 175 patients who underwent MRI enhancement scanning (from September 2016.9 to October 2020). They were divided into MVI positive, MVI negative, Grade-high and Grade-low groups. Results The AFP of 175 HCC patients distributed in MVI positive and negative groups, Grade-low and Grade-high groups were statistically significant (P = 0.002 and 0.03, respectively). Multiple HCC lesions were more common in MVI positive and Grade-high groups. Correspondingly, more single lesions were found in MVI negative and Grade-low groups (P = 0.005 and 0.019, respectively). Capsule on MRI was more common in MVI negative and Grade-high groups, and the difference was statistically significant (P = 0.02 and 0.011, respectively). There were statistical differences in the distribution of three MRI signs: artistic rim enhancement, artistic peripheral enhancement, and tumor margin between MVI positive and MVI negative groups (P = 0.001,

Background: Accurate prediction of the histological grade and Ki-67 expression of soft tissue sarcoma (STS) before surgery is essential for the subsequent diagnosis, treatment, and prognostic evaluation of patients. Purpose: To evaluate intravoxel incoherent motion (IVIM) and diffusion kurtosis imaging (DKI) in predicting the histological grade and Ki-67 expression of STS. Material and Methods: A total of 40 patients underwent 3-T MRI, including conventional sequences; IVIM and DKI parameters were obtained. All patients were divided into a low-grade (grade 1 and grade 2) group and a high-grade (grade 3) group through pathological analysis. Ki-67 expression of each lesion was calculated. Chi-square test, independent sample t -test, Mann–Whitney U test, Pearson, Spearman, and receiver operating characteristic curve analysis were performed. Results: There were 17 patients in the low-grade group and 23 in the high-grade group. Ki-67 expression was in the range of 10%–80%. D value was inversely correlated with Ki-67 expression. MK value showed a moderate positive correlation with Ki-67 expression. Regarding histological grading, only the peritumoral enhancement was statistically different between low- and high-grade STS on conventional MRI (P =0.024). The high-grade group had significantly higher MK value and lower D and MD value than the low-grade group. MK value showed the best diagnostic performance. The combination of MK and MD yielded the highest specificity (88.24%), and the combination of D, MK, and MD yielded the best area under the curve value (0.841) and sensitivity (95.65%). Conclusion: IVIM and DKI parameters were correlated with Ki-67 expression and could help differentiate between low- and high-grade STS. [ABSTRACT FROM AUTHOR]

Purpose. To evaluate the value of positron emission tomography/computed tomography (PET/CT) combined with high-resolution CT (HRCT) in determining the degree of differentiation of lung adenocarcinoma. Methods. From January 2018 to January 2022, 88 patients with solid density nodules that are lung adenocarcinoma were surgically treated. All patients were examined using HRCT and PET/CT before surgery. During HRCT, two independent observers assessed the presence of lobulation, spiculation, pleural indentation, vascular convergence, and air bronchial signs (bronchial distortion and bronchial disruption). The diameter and CT value of the nodules were measured simultaneously. During PET/CT, the maximum standard uptake value (SUVmax), mean standard uptake value (SUVmean), metabolic tumor volume (MTV), and total lesion glycolysis (TLG) of the nodules were measured. The risk factors of pathological classification were predicted by logistic regression analysis. Results. All 88 patients (mean age 60 ± 8 years; 44 males and 44 females) were evaluated. The average nodule size was 2.6 ± 1.1 cm. The univariate analysis showed that carcinoembryonic antigen (CEA), pleural indentation, vascular convergence, bronchial distortion, and higher SUVmax were more common in poor differentiated lung adenocarcinoma, and in the multivariate analysis, pleural indentation, vascular convergence, and SUVmax were predictive factors. The combined diagnosis using these three factors showed that the area under the curve (AUC) was 0.735. Conclusion. SUVmax >6.99 combined with HRCT (pleural indentation sign and vascular convergence sign) is helpful to predict the differentiation degree of lung adenocarcinoma dominated by solid density. [ABSTRACT FROM AUTHOR]

Simple Summary: Biopsy is an essential step in the diagnosis of myxoid liposarcoma (MLs) since the histological grade is a strong determinant of the appropriate treatment in the management of this pathology. The aim of our retrospective study was to investigate the diagnostic accuracy of Tru-cut® biopsy (TCB) and the potential impact on a patient's survival in the case of misdiagnosis. We established that in MLs, diagnosis with TCB might differ from that of surgical specimens, with a histological grade concordance rate of 64% (Kappa 0.30). Neoadjuvant treatments were associated with pathological downgrading with a lower effect of chemotherapy alone compared to neoadjuvant-combined treatments, although such discordance did not modify the prognosis. In the group of patients not treated in neoadjuvant settings, the sensitivity and specificity of TCB were 57% and 100%, respectively. TCB results are useful in leading the clinician in the diagnostic pathway thought; the diagnosis of MLs should be supported by other diagnostic techniques. (1) Background: Histological diagnosis and tumor grading are major prognostic and predictive factors in soft tissue sarcomas (STS), as they dictate the treatment strategies with a direct impact on patient survival. This study aims to investigate the grading accuracy, sensitivity, and specificity of Tru-Cut® biopsy (TCB) in primary localized myxoid liposarcomas (MLs) of the extremities and its impact on patient prognosis. (2) Methods: Patients with ML undergoing TCB and a subsequent tumor resection between 2007 and 2021 were evaluated. Concordance between the preoperative assessment and definitive histology was calculated with a weighted Cohen's kappa coefficient. Sensitivity, specificity, and diagnostic accuracy were calculated. (3) Results: Of 144 biopsies, the histological grade concordance rate was 63% (Kappa 0.2819). Neoadjuvant chemotherapy and/or radiotherapy impacted concordance with a downgrading effect in high-grade tumors. Among forty patients not treated in neoadjuvant settings, the sensitivity of TCB was 57%, the specificity was 100%, and the overall predictive values of positive and negative TCB were 100% and 50%, respectively. Misdiagnosis did not impact overall survival. (4) Conclusions: TCB may underestimate ML grading due to tumor heterogeneity. Neoadjuvant ChT and/or radiotherapy are associated with pathological downgrading; however, discordance in diagnosis does not modify patient prognosis because systemic treatment decision-making also includes other variables. [ABSTRACT FROM AUTHOR]

This study aimed to identify microRNAs associated with histological grade using comprehensive microRNA analysis data obtained by next-generation sequencing from early-stage invasive breast cancer. RNA-seq data from normal breast and breast cancer samples were compared to identify candidate microRNAs with differential expression using bioinformatics. A total of 108 microRNAs were significantly differentially expressed in normal breast and breast cancer tissues. Using clinicopathological information and microRNA sequencing data of 430 patients with breast cancer from The Cancer Genome Atlas (TCGA), the differences in candidate microRNAs between low- and high-grade tumors were identified. Comparing the expression of the 108 microRNAs between low- and high-grade cases, 25 and 18 microRNAs were significantly upregulated and downregulated, respectively, in high-grade cases. Clustering analysis of the TCGA cohort using these 43 microRNAs identified two groups strongly predictive of histological grade. miR-3677 is a microRNA upregulated in high-grade breast cancer. The outcome analysis revealed that patients with high miR-3677 expression had significantly worse prognosis than those with low miR-3677 expression. This study shows that microRNAs are associated with histological grade in early-stage invasive breast cancer. These findings contribute to the elucidation of a new mechanism of breast cancer growth regulated by specific microRNAs.

Abstract Objective Mucoepidermoid carcinoma (MEC) is the most common malignancy of the parotid gland, but the outcome depends on the histological grade. Therefore, the aim of this study was to evaluate MEC on the basis of histological grade. Study Design Retrospective analysis. Methods We performed a retrospective analysis of data from patients whose initial treatment for MEC of the parotid gland was performed at our department between 1999 and 2021. We examined the association between the Armed Forces Institute of Pathology (AFIP) grade and outcome. Results The AFIP grades were as follows: low, 26 cases; intermediate, 9 cases; and high, 31 cases. About 50% of cases were correctly diagnosed as malignant, and both grade and histology were accurately determined by fine‐needle aspiration cytology in 20% of cases. The 5‐year disease‐free survival rate was 95.5% and 53.8% in the low‐/intermediate‐ and high‐grade cases, respectively. In the high‐grade group, cases with recurrence were found to have a higher rate of lymph nodes metastasis than cases without recurrence. Furthermore, in this high‐grade group, total sacrifice of the facial nerve did not reduce local recurrence. However, radical resection in the cases without tumor invasion to the nerve has decreased the local recurrence rate. The CRTC1‐MAML2 fusion gene was expressed in 42.3% of low‐/intermediate‐ and 14.3% of high‐grade cases. Conclusions The survival rate in MEC was quite different between the low‐/intermediate‐ and high‐grade cases. However, the rate of correct assessment of the grade by fine‐needle aspiration cytology was poor. In high‐grade cases, total sacrifice of the facial nerve may improve the rate of local recurrence in cases without invasion of the main trunk of the nerve. Expression of the CRTC1‐MAML2 fusion gene could be helpful in not only the assessment of grade but the prediction of recurrence. Level of Evidence 4

Purpose To evaluate the value of positron emission tomography/computed tomography (PET/CT) combined with high-resolution CT (HRCT) in determining the degree of differentiation of lung adenocarcinoma. Methods From January 2018 to January 2022, 88 patients with solid density nodules that are lung adenocarcinoma were surgically treated. All patients were examined using HRCT and PET/CT before surgery. During HRCT, two independent observers assessed the presence of lobulation, spiculation, pleural indentation, vascular convergence, and air bronchial signs (bronchial distortion and bronchial disruption). The diameter and CT value of the nodules were measured simultaneously. During PET/CT, the maximum standard uptake value (SUVmax), mean standard uptake value (SUVmean), metabolic tumor volume (MTV), and total lesion glycolysis (TLG) of the nodules were measured. The risk factors of pathological classification were predicted by logistic regression analysis. Results All 88 patients (mean age 60 ± 8 years; 44 males and 44 females) were evaluated. The average nodule size was 2.6 ± 1.1 cm. The univariate analysis showed that carcinoembryonic antigen (CEA), pleural indentation, vascular convergence, bronchial distortion, and higher SUVmax were more common in poor differentiated lung adenocarcinoma, and in the multivariate analysis, pleural indentation, vascular convergence, and SUVmax were predictive factors. The combined diagnosis using these three factors showed that the area under the curve (AUC) was 0.735. Conclusion SUVmax >6.99 combined with HRCT (pleural indentation sign and vascular convergence sign) is helpful to predict the differentiation degree of lung adenocarcinoma dominated by solid density.

Introduction: The International Association for the Study of Lung Cancer proposed a novel grading system of invasive pulmonary adenocarcinoma (IPA), but the application of this grading system and its genotypic characterization in the real diagnostic scenario has never been reported. Methods: We prospectively collected and analyzed the clinicopathological and genotypic features of a cohort of 9353 consecutive patients with resected IPA, including 7134 patients with detection of common driver mutation. Results: In the entire cohort, 3 (0.3%) of lepidic, 1207 (19.0%) of acinar, and 126 (23.6%) of papillary predominant IPAs were diagnosed as grade 3. In chronological order, an evident downtrend of the proportion of grade 2 was observed in chronological order. Conversely, the diagnostic ratio of grade 1 (8.0%–14.5%) and grade 3 (27.9%–32.3%) experienced a gradual rise. EGFR mutation was more frequently detected in grade 2 (77.5%) and grade 1 (69.7%) IPA than grade 3 (53.7%, p < 0.001), whereas the mutation rates of KRAS, BRAF, ALK, and ROS1 were higher in grade 3 IPA. More importantly, the rate of EGFR mutation gradually fell as the proportion of high-grade components increased, to 24.3% in IPA with more than 90% high-grade components. Conclusions: The grading system for IPA could be applied to stratify patients with different clinicopathological and genotypic features in a real diagnostic scenario.

BackgroundTumor grade is associated with the treatment and prognosis of endometrial cancer (EC). The accurate preoperative prediction of the tumor grade is essential for EC risk stratification. Herein, we aimed to assess the performance of a multiparametric magnetic resonance imaging (MRI)-based radiomics nomogram for predicting high-grade EC.MethodsOne hundred and forty-three patients with EC who had undergone preoperative pelvic MRI were retrospectively enrolled and divided into a training set (n =100) and a validation set (n =43). Radiomic features were extracted based on T2-weighted, diffusion-weighted, and dynamic contrast-enhanced T1-weighted images. The minimum absolute contraction selection operator (LASSO) was implemented to obtain optimal radiomics features and build the rad-score. Multivariate logistic regression analysis was used to determine the clinical MRI features and build a clinical model. We developed a radiomics nomogram by combining important clinical MRI features and rad-score. A receiver operating characteristic (ROC) curve was used to evaluate the performance of the three models. The clinical net benefit of the nomogram was assessed using decision curve analysis (DCA), net reclassification index (NRI), and integrated discrimination index (IDI).ResultsIn total, 35/143 patients had high-grade EC and 108 had low-grade EC. The areas under the ROC curves of the clinical model, rad-score, and radiomics nomogram were 0.837 (95% confidence interval [CI]: 0.754–0.920), 0.875 (95% CI: 0.797–0.952), and 0.923 (95% CI: 0.869–0.977) for the training set; 0.857 (95% CI: 0.741–0.973), 0.785 (95% CI: 0.592–0.979), and 0.914 (95% CI: 0.827–0.996) for the validation set, respectively. The radiomics nomogram showed a good net benefit according to the DCA. NRIs were 0.637 (0.214–1.061) and 0.657 (0.079–1.394), and IDIs were 0.115 (0.077–0.306) and 0.053 (0.027–0.357) in the training set and validation set, respectively.ConclusionThe radiomics nomogram based on multiparametric MRI can predict the tumor grade of EC before surgery and yield a higher performance than that of dilation and curettage.

Background: Stathmin is a member of microtubule-associated protein. Inhibition of Stathmin expression can interfere with tumour progression and also alter the sensitivity of tumour cells to microtubule-targeting agents. Thus, it could be a potential therapeutic target for planning new treatment strategies. Objective: To study expression of Stathmin in different histological grades of oral squamous cell carcinoma (OSCC) and its correlation with Ki67 index. Materials and Methods: This study was an observational retrospective and prospective study conducted during a period of two and half years from January 2015 to June 2017 at ESI-PGIMSR Maniktala, Kolkata where 52 cases of OSCC were studied. Haematoxylin and eosin sections were reviewed and representative paraffin blocks were selected. Immunostains were performed using antibody clones for Stathmin and Ki67. For Stathmin scoring, Segersten scoring system was applied. Statistical analysis was done by Graph Pad Prism using Krusher Wallis Test and one-way ANOVA test. Spearman's coefficient was used to establish corelation between Ki 67 and Stathmin overexpression. Results: In this study, it is found that strong Stathmin expression score (4–9) was detected mostly (82.35%) in moderately differentiated (MD) OSCC and poorly differentiated (PD) OSCC (100%), whereas in contrast, 60% of well-differentiated OSCC showed negative-to-weak Stathmin score (1–3). Mean Ki67-labelling index for well-differentiated carcinoma was 32.37%, for moderately differentiated carcinoma was 60.89, and poorly differentiated carcinoma was 86.15%, which demonstrated increased tumour cell proliferation with progression of histological grades of OSCC. Conclusion: Stathmin expression was higher in MD OSCC to PD OSCC compared to well-differentiated carcinoma and its overexpression was significantly correlated with Ki67 index. Thus, Stathmin is overexpressed in higher grades and is correlated with high proliferation of tumour with a potential role as therapeutic target. [ABSTRACT FROM AUTHOR]

Background: Histological grade has been demonstrated to be an important factor of breast cancer outcome and is associated with cell differentiation and is currently being evaluated via H&E-stained sections. Molecular biomarkers are essential to improve the accuracy of histological grading. ATBF1, a large transcription factor, has been considered a tumor suppressor gene with frequent mutations or deletions in multiple cancers. In breast cancer, ATBF1 was reported to function in cell differentiation and mammary development. However, its role in the clinic has rarely been reported. Methods: Breast cancer tissues (BCTs) and adjacent noncancerous tissues (ANCTs) were collected to analyze the expression of ATBF1 at the mRNA and protein levels. Three anti-ATBF1 antibodies recognizing independent peptides of ATBF1 (N-terminal end, middle region and C-terminal end) were applied for IHC staining. Small interfering RNA (siRNA) was used to silence ATBF1 expression and to investigate the roles of ATBF1 in MCF7 cells. Microarrays were introduced to analyze the differentially expressed genes, enriched GO terms and KEGG terms regulated by ATBF1 and its potential downstream genes, which were further confirmed in vitro and in clinical samples. Results: The expression of ATBF1 was reduced in BCTs at both the mRNA and protein levels compared with that in ANCTs. ATBF1 protein was predominantly localized in the nucleus of ANCTs but in the cytoplasm of BCTs. Both the mRNA and protein levels of ATBF1 were significantly correlated with histological grade. Consistently, knockdown of ATBF1 increased stemness marker expression and reduced differentiation markers in vitro. Further analysis identified WNT5A as an essential downstream gene of ATBF1 in breast cancer cells. Treatment of WNT5A disrupted cell proliferation induced by ATBF1 silencing. In BCTs, a significant correlation was observed between the expression of WNT5A and ATBF1. Conclusion: The results indicated that ATBF1 expression might be a useful diagnostic marker associated with histological grade and breast cancer malignancy. WNT5A and its signaling pathway are novel mechanisms by which ATBF1 contributes to breast cancer tumorigenesis. [ABSTRACT FROM AUTHOR]

Alterations in DNA methylation are critical for the carcinogenesis of ovarian tumors, especially ovarian carcinoma (OC). DNMT3B, a de novo DNA methyltransferase (DNMT), encodes for fifteen spliced protein products or isoforms. DNMT3B isoforms lack exons for the catalytic domain, with functional consequences on catalytic activity. Abnormal expression of DNMT3B isoforms is frequently observed in several types of cancer, such as breast, lung, kidney, gastric, liver, skin, leukemia, and sarcoma. However, the expression patterns and consequences of DNMT3B isoforms in OC are unknown. In this study, we analyzed each DNMT and DNMT3B isoforms expression by qPCR in 63 OC samples and their association with disease-free survival (DFS), overall survival (OS), and tumor progression. We included OC patients with the main histological subtypes of EOC and patients in all the disease stages and found that DNMTs were overexpressed in advanced stages (p-value < 0.05) and high-grade OC (p-value < 0.05). Remarkably, we found DNMT3B1 overexpression in advanced stages (p-value = 0.0251) and high-grade serous ovarian carcinoma (HGSOC) (p-value = 0.0313), and DNMT3B3 was overexpressed in advanced stages (p-value = 0.0098) and high-grade (p-value = 0.0004) serous ovarian carcinoma (SOC). Finally, we observed that overexpression of DNMT3B isoforms was associated with poor prognosis in OC and SOC. DNMT3B3 was also associated with FDS (p-value = 0.017) and OS (p-value = 0.038) in SOC patients. In addition, the ovarian carcinoma cell lines OVCAR3 and SKOV3 also overexpress DNMT3B3. Interestingly, exogenous overexpression of DNMT3B3 in OVCAR3 causes demethylation of satellite 2 sequences in the pericentromeric region. In summary, our results suggest that DNMT3B3 expression is altered in OC. [ABSTRACT FROM AUTHOR]

Background: One fifth of patients with epithelial ovarian cancers (EOC) present at an early stage (FIGO stage I & II). However, there is scarcity of literature on the outcomes and its predictors. The aim of the study was to assess relapse free survival (RFS), overall survival (OS) and its predictors in early stage EOC. Patients and Methods: In this retrospectively study, we included all patients with early-stage EOC diagnosed between January 2010 and December 2018. Patients with synchronous malignancies were excluded. Clinical profile, clinico-pathological characteristics and treatment details were recorded. Patient underwent initial surgery followed by adjuvant chemotherapy in high-risk disease. Patients with stage IC, or stage II or clear cell histology or high-grade histology irrespective of stage/histological subtype were defined as high-risk disease. Fertility sparing surgery (FSS) [unilateral salpingo-oopherectomy with complete surgical staging] was performed in patient willing to preserve fertility. Primary objective was to assess RFS and OS in all patients with early stage EOC. Secondary objectives were to assess RFS and OS in early stage EOC with high-risk disease, predictors of RFS and OS, and outcomes of FSS. Survival probabilities were estimated according to Kaplan-Meier and compared by the log rank test. Cox's regression model was used to analyze the significance of various factors affecting relapse free survival (RFS) and overall survival (OS). Results: 195 patients with early stage EOC were recruited with median age of 47 years (range, 16–80 years). FIGO stage I and stage II were seen in 72 % and 18 % patients respectively. Serous subtype was reported in 58 % and high-grade histology in 66 %. 184 patients (94.0%) underwent optimal staging surgery, including 27 (14%) with fertility sparing surgery (FSS). 133 (91.7 %) of 145 patients with high-risk disease received adjuvant chemotherapy (paclitaxel and carboplatin), while 12 (8.3 %) patients opted to remain on observation. At median follow up of 56 months (95 % CI, 46–64 months), 49 (25 %) patients relapsed [including 3 of 27 (11.1 %) who underwent FSS], 18 patients died of progressive disease, while 31 patients were alive and disease free. Estimated OS at 5 years is 87.6 % (95 % CI 79.9–92.5) and RFS is 73.2 % (95 % CI 64.7–80.0). On multivariate analysis tumor grade was predictive of RFS (HR 2.9, p

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Background: We designed and validated the value of multiple radiomics models for diagnosing histological grade of pancreatic ductal adenocarcinoma (PDAC), holding a promise of assisting in precision medicine and providing clinical therapeutic strategies. Methods: 198 PDAC patients receiving surgical resection and pathological confirmation were enrolled and classified as 117 low-grade PDAC and 81 high-grade PDAC group. An external validation group was used to assess models' performance. Available radiomics features were selected using GBDT algorithm on the basis of the arterial and venous phases, respectively. Five different machine learning models were built including k-nearest neighbour, logistic regression, naive bayes model, support vector machine, and random forest using ten times tenfold cross-validation. Multivariable logistic regression analysis was applied to establish clinical model and combined model. The models' performance was assessed according to its predictive performance, calibration curves, and decision curves. A nomogram was established for visualization. Survival analysis was conducted for stratifying the overall survival prior to treatment. Results: In the training group, the RF model demonstrated the optimal predictive ability and robustness with an AUC of 0.943; the SVM model achieved the secondary performance, followed by Bayes model. In the external validation group, these three models (Bayes, RF, SVM) also achieved the top three predictive ability. A clinical model was built by selected clinical features with an AUC of 0.728, and combined model was established by an RF model and a clinical model with an AUC of 0.961. The log-rank test revealed that the low-grade group survived longer than the high-grade group. Conclusions: The multiphasic CECT radiomics models offered an accurate and noninvasive perspective to differentiate histological grade in PDAC and advantages of machine learning models including RF, SVM and Bayes were more remarkable. [ABSTRACT FROM AUTHOR]

Background: To predict the histological grade and microvascular invasion (MVI) in patients with HCC.Methods: A retrospective analysis was conducted on 175 patients who underwent MRI enhancement scanning (from September 2016.9 to October 2020). They were divided into MVI positive, MVI negative, Grade-high and Grade-low groups.Results: The AFP of 175 HCC patients distributed in MVI positive and negative groups, Grade-low and Grade-high groups were statistically significant (P = 0.002 and 0.03, respectively). Multiple HCC lesions were more common in MVI positive and Grade-high groups. Correspondingly, more single lesions were found in MVI negative and Grade-low groups (P = 0.005 and 0.019, respectively). Capsule on MRI was more common in MVI negative and Grade-high groups, and the difference was statistically significant (P = 0.02 and 0.011, respectively). There were statistical differences in the distribution of three MRI signs: artistic rim enhancement, artistic peripheral enhancement, and tumor margin between MVI positive and MVI negative groups (P = 0.001, < 0.001, and < 0.001, respectively). Tumor hypointensity on HBP was significantly different between MVI positive and negative groups (P < 0.001).Conclusions: Our research shows that preoperative enhanced imaging can be used to predict MVI and tumor differentiation grade of HCC. The prognosis of MVI-negative group was better than that of MVI-positive group. [ABSTRACT FROM AUTHOR]